# Importing statements and display configurations
import sqlite3
import pandas as pd
import numpy as np 
import plotly.express as px
import plotly.graph_objects as go 
from IPython.display import display

# Set max_colwidth to None to display full contents of each cell
pd.set_option('display.max_colwidth', None)

# Loading data from csv file into a DataFrame
csv_file_path = 'csv_final_for_jupyter_notebook.csv'
df = pd.read_csv(csv_file_path, delimiter=',')

# Establishing connection to SQLite database
conn = sqlite3.connect(':memory:')  # In-memory database for demonstration purposes

# Writing DataFrame to SQLite database, setting table name "final"
df.to_sql('final', conn, index=False)

1000

# Displaying the DataFrame head as a table using IPython display
display(df.head())

	F1	Movie Name	Year	Duration	Rating	Metascore	Votes	Gross	Description
0	0	The Shawshank Redemption	1994	142	9.3	81.0	34,709	28.34	Two imprisoned men bond over a number of years, finding solace and eventual redemption through acts of common decency.
1	1	The Godfather	1972	175	9.2	100.0	34,709	134.97	The aging patriarch of an organized crime dynasty in postwar New York City transfers control of his clandestine empire to his reluctant youngest son.
2	2	The Dark Knight	2008	152	9.0	84.0	34,709	534.86	When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.
3	3	The Lord of the Rings: The Return of the King	2003	201	9.0	94.0	34,709	377.85	Gandalf and Aragorn lead the World of Men against Sauron's army to draw his gaze from Frodo and Sam as they approach Mount Doom with the One Ring.
4	4	Schindler's List	1993	195	9.0	94.0	34,709	96.90	In German-occupied Poland during World War II, industrialist Oskar Schindler gradually becomes concerned for his Jewish workforce after witnessing their persecution by the Nazis.

# General Info
display(df.info())

# Basic statistics
display('General Description', df.describe())

# Shape of the dataframe
display("The shape of the dataframe", df.shape)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 9 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   F1           1000 non-null   int64  
 1   Movie Name   1000 non-null   object 
 2   Year         1000 non-null   int64  
 3   Duration     1000 non-null   int64  
 4   Rating       1000 non-null   float64
 5   Metascore    836 non-null    float64
 6   Votes        1000 non-null   object 
 7   Gross        817 non-null    float64
 8   Description  1000 non-null   object 
dtypes: float64(3), int64(3), object(3)
memory usage: 70.4+ KB

None

'General Description'

	F1	Year	Duration	Rating	Metascore	Gross
count	1000.000000	1000.000000	1000.000000	1000.000000	836.000000	817.000000
mean	499.500000	1991.190000	123.747000	7.962700	78.686603	70.132497
std	288.819436	23.924972	28.468816	0.276759	12.052971	113.765949
min	0.000000	1920.000000	45.000000	7.600000	28.000000	0.000000
25%	249.750000	1975.000000	103.000000	7.700000	71.000000	3.270000
50%	499.500000	1999.000000	120.000000	7.900000	80.000000	23.650000
75%	749.250000	2010.000000	138.000000	8.100000	88.000000	85.080000
max	999.000000	2022.000000	321.000000	9.300000	100.000000	936.660000

'The shape of the dataframe'

(1000, 9)

# Creating a cursor object  for querying data
cursor = conn.cursor()

# Find the average of Duration in min, Metascore, Rating, Gross in millions

query = """
SELECT 
  AVG(Duration) As Average_Duration_min, 
  ROUND(
  AVG(Metascore),
  2
  ) As Average_Metascore --Two decimal places
  , 
  ROUND(
    AVG(Rating), 
    2
  ) As Average_Rating --Two decimal places
  , 
  ROUND(
    AVG(Gross), 
    2
  ) As Average_Gross_mill  -- millions with two decimal places
FROM 
  final
"""
# Fetching results into a DataFrame
results_df = pd.read_sql_query(query, conn)

# Displaying the DataFrame as a table using IPython display
display(results_df)

	Average_Duration_min	Average_Metascore	Average_Rating	Average_Gross_mill
0	123.747	78.69	7.96	70.13

# Counting the number of movies released after 1999, grouped by year

query = """
SELECT 
  [Year], 
  COUNT(*) AS movies_number 
FROM 
  final 
WHERE 
  [Year] >= 2000 
GROUP BY 
  [Year] 
ORDER BY 
  [Year] ASC

"""

results_df = pd.read_sql_query(query, conn)
display(results_df)

# Plotting the distribution of movies released in every year
fig = px.bar(
    results_df,
    x='Year',
    y='movies_number',
    title="Number of Movies Released by Year",
    color_discrete_sequence=['green'],
    width=1000,
    height=500
)

fig.show()

	Year	movies_number
0	2000	18
1	2001	26
2	2002	16
3	2003	22
4	2004	32
5	2005	17
6	2006	25
7	2007	25
8	2008	20
9	2009	26
10	2010	21
11	2011	17
12	2012	23
13	2013	27
14	2014	32
15	2015	24
16	2016	28
17	2017	17
18	2018	21
19	2019	26
20	2020	8
21	2021	10
22	2022	7

# Top 10 higher rated movies

query = """
SELECT 
  [Movie Name], 
  [Rating] 
FROM 
  final 
ORDER BY 
  [Rating] DESC
  LIMIT 10
"""

results_df = pd.read_sql_query(query, conn)
display(results_df)

results_df = results_df.iloc[::-1]# reversing the dataframe for plotly bar
fig = px.bar(
    results_df,
    x='Rating',
    y='Movie Name',
    color='Rating',
    text_auto=True,
    title='Top 10 rated Movies',
    width =800,
    height=500
)

fig.show()

	Movie Name	Rating
0	The Shawshank Redemption	9.3
1	The Godfather	9.2
2	The Dark Knight	9.0
3	The Lord of the Rings: The Return of the King	9.0
4	Schindler's List	9.0
5	The Godfather: Part II	9.0
6	12 Angry Men	9.0
7	Jai Bhim	8.9
8	Pulp Fiction	8.9
9	Inception	8.8

# Top 10 higher grossing movies

query = """
SELECT 
  [Movie Name], 
  [Gross] 
FROM 
  final 
ORDER BY 
  [Gross] DESC
LIMIT 10
"""

results_df = pd.read_sql(query,conn)
display(results_df)


results_df =results_df.iloc[::-1]# reversing the dataframe for plotly bar
fig = px.bar(
    results_df,
    x='Gross',
    y='Movie Name',
    color='Gross',
    text_auto=True,
    title='Top 10 Gross Movies',
    width =800,
    height=500
)

fig.show()

	Movie Name	Gross
0	Star Wars: Episode VII - The Force Awakens	936.66
1	Avengers: Endgame	858.37
2	Spider-Man: No Way Home	804.75
3	Avatar	760.51
4	Avengers: Infinity War	678.82
5	Titanic	659.33
6	The Avengers	623.28
7	Incredibles 2	608.58
8	The Dark Knight	534.86
9	Rogue One: A Star Wars Story	532.18

# Rating of Top 10 higher metascore movies

query = """
SELECT 
  [Movie Name], 
  [Metascore],
  [Rating]
FROM 
  final 
ORDER BY 
  [Gross] DESC
LIMIT 10
"""

results_df = pd.read_sql(query,conn)
display(results_df)



fig = px.bar(
    results_df,
    x='Metascore',
    y='Movie Name',
    color='Rating',
    text_auto=True,
    title=' Rating f Top 10 Metascore Movies',
    width =800,
    height=500
)

fig.show()

	Movie Name	Metascore	Rating
0	Star Wars: Episode VII - The Force Awakens	80.0	7.8
1	Avengers: Endgame	78.0	8.4
2	Spider-Man: No Way Home	71.0	8.3
3	Avatar	83.0	7.8
4	Avengers: Infinity War	68.0	8.4
5	Titanic	75.0	7.9
6	The Avengers	69.0	8.0
7	Incredibles 2	80.0	7.6
8	The Dark Knight	84.0	9.0
9	Rogue One: A Star Wars Story	65.0	7.8

# List of long movies (duration >=180 min)

query ="""
SELECT 
  [Movie Name], 
  [Duration] 
FROM 
  final 
WHERE 
  [Duration] >= 180 
ORDER BY 
  [Duration] DESC
"""

results_df = pd.read_sql(query, conn)
display(results_df)

	Movie Name	Duration
0	Gangs of Wasseypur	321
1	Zack Snyder's Justice League	242
2	Hamlet	242
3	Gone with the Wind	238
4	Once Upon a Time in America	229
5	Lagaan: Once Upon a Time in India	224
6	The Ten Commandments	220
7	Lawrence of Arabia	218
8	Ben-Hur	212
9	The Irishman	209
10	Seven Samurai	207
11	Andrei Rublev	205
12	The Godfather: Part II	202
13	Malcolm X	202
14	The Lord of the Rings: The Return of the King	201
15	Doctor Zhivago	197
16	Spartacus	197
17	Winter Sleep	196
18	Schindler's List	195
19	Titanic	194
20	The Right Stuff	193
21	Veer-Zaara	192
22	Gandhi	191
23	The Green Mile	189
24	Swades	189
25	JFK	189
26	Fanny and Alexander	188
27	Magnolia	188
28	Short Cuts	188
29	RRR	187
30	Bhaag Milkha Bhaag	186
31	The Leopard	186
32	Kal Ho Naa Ho	186
33	Barry Lyndon	185
34	M.S. Dhoni: The Untold Story	184
35	Dil Chahta Hai	183
36	The Deer Hunter	183
37	Avengers: Endgame	181
38	Dilwale Dulhania Le Jayenge	181
39	Dances with Wolves	181
40	Fiddler on the Roof	181
41	The Wolf of Wall Street	180
42	Blood In, Blood Out	180
43	Blue Is the Warmest Colour	180

# Top 10 higher grossing police movies by description 

query = """
SELECT 
  [Movie Name], 
  [Gross], 
  [Description] 
FROM 
  final 
WHERE 
  (
    [Description] LIKE '%police%' 
    OR [Description] LIKE '%thief%' 
    OR [Description] LIKE '%robber%'
  ) -- Police movies
  AND [Gross] IS NOT NULL --Not including NULL Gross values
ORDER BY 
  [Gross] DESC
LIMIT 10
"""

results_df = pd.read_sql(query, conn)
display(results_df)

fig=px.bar(
    results_df,
    x='Movie Name',
    y='Gross',
    color='Gross',
    title='Top 10 higher grossing police movies',
    text_auto=True,
    width=800,
    height=500
)

fig.show()

	Movie Name	Gross	Description
0	Inception	292.58	A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into the mind of a C.E.O., but his tragic past may doom the project and his team to disaster.
1	Tangled	200.82	The magically long-haired Rapunzel has spent her entire life in a tower, but now that a runaway thief has stumbled upon her, she is about to discover the world for the first time, and who she really is.
2	As Good as It Gets	148.48	A single mother and waitress, a misanthropic author, and a gay artist form an unlikely friendship after the artist is assaulted in a robbery.
3	The Departed	132.38	An undercover cop and a mole in the police attempt to identify each other while infiltrating an Irish gang in South Boston.
4	Minority Report	132.07	In a future where a special police unit is able to arrest murderers before they commit their crimes, an officer from that unit is himself accused of a future murder.
5	Butch Cassidy and the Sundance Kid	102.31	Wyoming, early 1900s. Butch Cassidy and The Sundance Kid are the leaders of a band of outlaws. After a train robbery goes wrong they find themselves on the run with a posse hard on their heels. Their solution - escape to Bolivia.
6	Inside Man	88.51	A police detective, a bank robber, and a high-power broker enter high-stakes negotiations after the criminal's brilliant heist spirals into a hostage situation.
7	The Naked Gun: From the Files of Police Squad!	78.76	Incompetent police Detective Frank Drebin must foil an attempt to assassinate Queen Elizabeth II.
8	L.A. Confidential	64.62	As corruption grows in 1950s Los Angeles, three policemen - one strait-laced, one brutal, and one sleazy - investigate a series of murders with their own brand of justice.
9	Prisoners	61.00	When Keller Dover's daughter and her friend go missing, he takes matters into his own hands as the police pursue multiple leads and the pressure mounts.

# Top 10 higher Metascore crime movies by description 

query = """
SELECT 
  [Metascore], 
  [Movie Name], 
  [Description] 
FROM 
  final 
WHERE 
  (
    [Description] LIKE '%murder%' 
    OR [Description] LIKE '%crime%' 
    OR [Description] LIKE '%killer%'
  ) -- Crime movies
  AND [Metascore] IS NOT NULL --Not including NULL Metascore values
ORDER BY 
  [Metascore] DESC
LIMIT 10
"""

results_df = pd.read_sql(query, conn)
display(results_df)

fig=px.bar(
    results_df,
    x='Movie Name',
    y='Metascore',
    color='Metascore',
    title='Top 10 higher Metascore crime movies',
    text_auto=True,
    width=800,
    height=500
    
)
fig.show()

	Metascore	Movie Name	Description
0	100.0	The Godfather	The aging patriarch of an organized crime dynasty in postwar New York City transfers control of his clandestine empire to his reluctant youngest son.
1	100.0	Rear Window	A wheelchair-bound photographer spies on his neighbors from his Greenwich Village courtyard apartment window, and becomes convinced one of them has committed murder, despite the skepticism of his fashion-model girlfriend.
2	99.0	Touch of Evil	A stark, perverse story of murder, kidnapping, and police corruption in a Mexican border town.
3	98.0	Rashomon	The rape of a bride and the murder of her samurai husband are recalled from the perspectives of a bandit, the bride, the samurai's ghost and a woodcutter.
4	97.0	Rififi	Four men plan a technically perfect crime, but the human element intervenes...
5	97.0	The Maltese Falcon	San Francisco private detective Sam Spade takes on a case that involves him with three eccentric criminals, a gorgeous liar, and their quest for a priceless statuette, with the stakes rising after his partner is murdered.
6	96.0	12 Angry Men	The jury in a New York City murder trial is frustrated by a single member whose skeptical caution forces them to more carefully consider the evidence before jumping to a hasty verdict.
7	95.0	Double Indemnity	A Los Angeles insurance representative lets an alluring housewife seduce him into a scheme of insurance fraud and murder that arouses the suspicion of his colleague, an insurance investigator.
8	94.0	Elevator to the Gallows	A self-assured businessman murders his employer, the husband of his mistress, which unintentionally provokes an ill-fated chain of events.
9	94.0	The Irishman	Hitman Frank Sheeran looks back at the secrets he kept as a loyal member of the Bufalino crime family.

# List of World war two movies by description 

query = """
SELECT 
  [Year], 
  [Movie Name],
  [Rating],
  [Description] 
FROM 
  final 
WHERE 
  (
    [Description] LIKE '%nazi%' 
    OR [Description] LIKE '%world war 2%' 
    OR [Description] LIKE '%world war two%'
  ) 
  AND [Description] NOT LIKE '%neo-nazi%' --Not including post war neo_nazi movies
ORDER BY 
  [Rating] DESC

"""

results_df = pd.read_sql(query, conn)
display(results_df)

fig=px.scatter(
    results_df,
    x='Year',
    y='Rating',
    color='Movie Name',
    width=800,
    height=500
    
)
# Updating the marker size
fig.update_traces(marker=dict(size=results_df['Rating']*2,
                              line=dict(width=2,
                                        color='SandyBrown')),
                  selector=dict(mode='markers'))

fig.show()

	Year	Movie Name	Rating	Description
0	1993	Schindler's List	9.0	In German-occupied Poland during World War II, industrialist Oskar Schindler gradually becomes concerned for his Jewish workforce after witnessing their persecution by the Nazis.
1	1942	Casablanca	8.5	A cynical expatriate American cafe owner struggles to decide whether or not to help his former lover and her fugitive husband escape the Nazis in French Morocco.
2	1981	Indiana Jones and the Raiders of the Lost Ark	8.4	In 1936, archaeologist and adventurer Indiana Jones is hired by the U.S. government to find the Ark of the Covenant before Adolf Hitler's Nazis can obtain its awesome powers.
3	2009	Inglourious Basterds	8.3	In Nazi-occupied France during World War II, a plan to assassinate Nazi leaders by a group of Jewish U.S. soldiers coincides with a theatre owner's vengeful plans for the same.
4	1961	Judgment at Nuremberg	8.3	In 1948, an American court in occupied Germany tries four Nazis judged for war crimes.
5	2004	Downfall	8.2	Traudl Junge, the final secretary for Adolf Hitler, tells of the Nazi dictator's final days in his Berlin bunker at the end of WWII.
6	1989	Indiana Jones and the Last Crusade	8.2	In 1938, after his father Professor Henry Jones, Sr. goes missing while pursuing the Holy Grail, Professor Henry "Indiana" Jones, Jr. finds himself up against Adolf Hitler's Nazis again to stop them from obtaining its powers.
7	1942	To Be or Not to Be	8.2	During the Nazi occupation of Poland, an acting troupe becomes embroiled in a Polish soldier's efforts to track down a German spy.
8	1945	Rome, Open City	8.0	During the Nazi occupation of Rome in 1944, the Resistance leader, Giorgio Manfredi, is chased by the Nazis as he seeks refuge and a way to escape.
9	1946	Notorious	7.9	The daughter of a convicted Nazi spy is asked by American agents to gather information on a ring of Nazi scientists in South America. How far will she have to go to ingratiate herself with them?
10	1972	Cabaret	7.8	A female girlie club entertainer in Weimar Republic era Berlin romances two men while the Nazi Party rises to power around them.
11	2006	Black Book	7.7	In the Nazi-occupied Netherlands during World War II, a Jewish singer infiltrates the regional Gestapo headquarters for the Dutch resistance.
12	1968	Where Eagles Dare	7.6	Allied agents stage a daring raid on a castle where the Nazis are holding American brigadier general George Carnaby prisoner, but that's not all that's really going on.

# Grouping the data by 'Year' and counting the occurrences
year_counts= df['Year'].value_counts().reset_index()
year_counts.columns = ['Year', 'Count']

# Sorting the dataframe by 'Year'
year_counts = year_counts.sort_values(by='Year')

# Plotting the distribution of movies released in every year
fig = px.bar(
    year_counts,
    x='Year',
    y='Count',
    title="Number of Movies Released by Year",
    color_discrete_sequence=['green'],
    width=1000,
    height=500
)

fig.show()

fig =px.scatter(
    year_counts,
    x='Count',
    y='Year',
    color='Year',
    title="Number of Movies Released by Year",
    width=800,
    height=500
)

# Updating the marker size
fig.update_traces(marker=dict(size=year_counts['Count'],
                              line=dict(width=2,
                                        color='SandyBrown')),
                  selector=dict(mode='markers'))
fig.show()

# Ploting  histogram of metascore distribution including a box plot

fig = px.histogram(
    df,
    x='Metascore',
    nbins=20,
    text_auto=True,
    title='Metascore distribution',
    color_discrete_sequence=['blue'],
    width=1000,
    height=500,
    marginal='box'      
)

fig.show()

# Ploting  pie chart of gross distribution 

# Define the bin edges
bin_edges = [0, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]

# Define bin labels
bin_labels = ['0-100', '101-200', '201-300', '301-400', '401-500', 
              '501-600', '601-700', '701-800', '801-900', '901-1000']

# Classify gross values into bins
df['Gross_Class'] = pd.cut(df['Gross'], bins=bin_edges, labels=bin_labels, right=False)

# Calculate the count of movies in each gross class
gross_class_counts = df['Gross_Class'].value_counts().reset_index()
gross_class_counts.columns = ['Gross_Class', 'Count']

# Plotting the pie chart
fig = px.pie(
    gross_class_counts,
    names='Gross_Class',
    values='Count',
    title='Gross Distribution (0-1000) millions',
    width=800,
    height=500,
    hole=0.5
)

fig.show()

#Insert a new decade column for further decade analysis

# Add a new column of name Decade and  type int

alter_query="""
    ALTER TABLE final
    ADD COLUMN Decade INT
"""

# Execute alter query
cursor.execute(alter_query)

# Define the values of the new column
update_query="""
    UPDATE final
    SET Decade = (ROUND(Year/10)*10)
"""

# Execute update query
cursor.execute(update_query)

# Commiting the changes to the database
conn.commit()

# Verifying the changes by fetching the data from the updated table

query = """
SELECT * FROM final
"""

updated_df = pd.read_sql_query(query, conn)

# Display the updated DataFrame
display(updated_df.head())

display(updated_df.dtypes)

	F1	Movie Name	Year	Duration	Rating	Metascore	Votes	Gross	Description	Decade
0	0	The Shawshank Redemption	1994	142	9.3	81.0	34,709	28.34	Two imprisoned men bond over a number of years, finding solace and eventual redemption through acts of common decency.	1990
1	1	The Godfather	1972	175	9.2	100.0	34,709	134.97	The aging patriarch of an organized crime dynasty in postwar New York City transfers control of his clandestine empire to his reluctant youngest son.	1970
2	2	The Dark Knight	2008	152	9.0	84.0	34,709	534.86	When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.	2000
3	3	The Lord of the Rings: The Return of the King	2003	201	9.0	94.0	34,709	377.85	Gandalf and Aragorn lead the World of Men against Sauron's army to draw his gaze from Frodo and Sam as they approach Mount Doom with the One Ring.	2000
4	4	Schindler's List	1993	195	9.0	94.0	34,709	96.90	In German-occupied Poland during World War II, industrialist Oskar Schindler gradually becomes concerned for his Jewish workforce after witnessing their persecution by the Nazis.	1990

F1               int64
Movie Name      object
Year             int64
Duration         int64
Rating         float64
Metascore      float64
Votes           object
Gross          float64
Description     object
Decade           int64
dtype: object

# Grouping the data by 'Decade' and counting the occurrences
decade_counts= updated_df['Decade'].value_counts().reset_index()
decade_counts.columns = ['Decade', 'Count']

# Sorting the dataframe by 'Decade'
decade_counts = decade_counts.sort_values(by='Decade')

# Plotting the donut chart
fig = px.pie(
    decade_counts,
    names='Decade',
    values='Count',
    title='Percentage of Movies Released by Decade',
    width=800,
    height=500,
    hole=0.6  # Set the size of the hole to create the donut shape
)

fig.show()

# Average values by Decade

query = """
    SELECT
  Decade,
  ROUND(
  AVG(Duration),
  0
  ) As Average_Duration_min, 
  ROUND(
  AVG(Metascore),
  2
  ) As Average_Metascore --Two decimal places
  , 
  ROUND(
    AVG(Rating), 
    2
  ) As Average_Rating --two decimal places
  , 
  ROUND(
    AVG(Gross), 
    2
  ) As Average_Gross_mill  -- millions with two decimal places
FROM 
  final
GROUP BY Decade 
ORDER BY Decade ASC
"""
# Fetch results into a DataFrame
results_df = pd.read_sql_query(query, conn)

# Display the DataFrame as a table using IPython display
display(results_df)

	Decade	Average_Duration_min	Average_Metascore	Average_Rating	Average_Gross_mill
0	1920	85.0	95.00	8.14	1.85
1	1930	102.0	91.00	7.94	28.59
2	1940	109.0	92.52	8.02	7.91
3	1950	116.0	89.76	8.06	12.86
4	1960	128.0	83.59	7.96	31.35
5	1970	124.0	83.46	7.99	53.37
6	1980	122.0	76.89	7.96	60.70
7	1990	124.0	75.10	7.98	63.97
8	2000	124.0	74.56	7.91	72.56
9	2010	129.0	77.41	7.93	107.47
10	2020	145.0	77.61	8.17	320.26

# Average decade gross vs total gross

fig = go.Figure()

# Adding bar plot of average Gross by decade
fig.add_trace(trace=go.Bar(
    x=results_df['Decade'],
    y=results_df['Average_Gross_mill'],
    text=results_df['Average_Gross_mill'],
    marker_color=results_df['Average_Gross_mill'],
    name='Averege Gross'
))


# Adding the line of the total average Gross 7.13 millions
fig.add_trace(trace=go.Scatter(
    x=[1915, 2025],
    y=[7.13, 7.13],
    mode='lines',
    marker_color='black',
    name='Total average Gross line = 7.13 millions'
     ))

# Setting the general title and the size of the chart 
fig.update_layout(title='Average  Gross  by Decade',
                  width=1000,
                  height=500,
                  )

# Setting X-axis title and angle for decades display
fig.update_xaxes(title='Decade', tickangle = 45)
fig.show()